Executive Summary

This report analyses temperature and precipitation trends across four Egyptian governorates using ERA5 (1960-2024) and CHIRPS (1981-2024) data as well as remotely sensed data on drought conditions (ESI and NDVI). Key findings include:

  • Warming Trend: All governorates show clear warming trends, with average temperatures increasing 1.5-2.5°C since 1960. Heat waves have become more common and increased in intensity and length. The number of tropical nights (minimum temperature above 20° Celsius) and hot winter days (maximum temperature above 30° C) has substantially increased, posing a threat to human well-being and plant productivity.
  • Recent Acceleration: Temperature deviations from the 1960-2010 baseline have intensified since 2010.
  • Precipitation Variability: Rainfall patterns show high inter-annual variability with notable extreme dry and wet years.
  • Regional Variation: Both temperature and precipitation changes vary across governorates.

Data and Methods

Data Sources

  • Temperature Data: ERA5 reanalysis (1960-2024) at daily resolution.
  • Precipitation Data: CHIRPS (1981-2024) at daily resolution.
  • Agricultural Drought: Remotely sensed data indicating vegetative stress (ESI, NDVI, EVI).
  • Population Data: LandScan population distribution (2020).
  • Baseline Period: 1960-2010 for temperature; 1981-2024 mean for precipitation.

Weighting Methods

  1. Unweighted: Simple spatial average across each governorate.
  2. Population-weighted: Weighted by where people actually live within each governorate.,

Population weighting provides a more accurate representation of human exposure to climate changes.

Population and Cropland Distribution

#population palette
pal_pop <- colorNumeric(
  palette = "viridis",
  domain = log10(pop_2020_cropped_df$b1 + 1),
  na.color = "transparent")

#cropland palette
pal_crop <- colorFactor(
  palette = c("gray90", "#238b45"),
  levels = c("Other land use", "Cropland"),
  na.color = "transparent"
)

#leaflet map
leaflet() |>
  addProviderTiles(providers$CartoDB.Positron) |>
  addRasterImage(
    log10(pop_2020_cropped + 1),
    colors = pal_pop,
    opacity = 0.8,
    project = TRUE,
    group = "Population") |>
  addRasterImage(
    crop_2021_cropped_cat,
    colors = pal_crop,
    opacity = 0.8,
    project = TRUE,
    group = "Cropland") |>
  addPolygons(
    data = egypt_4gov,
    fill = FALSE,
    color = "black",
    weight = 1.5,
    label = ~ADM1_EN) |>
  addLegend(
    pal = pal_pop,
    values = values(log10(pop_2020_cropped + 1)),
    title = "Population (log scale)",
    labFormat = labelFormat(
      transform = function(x) round(10^x)),
    group = "Population"
  ) |>
  addLegend(
    pal = pal_crop,
    values = values(crop_2021_cropped_cat),
    title = "Land Use",
    group = "Cropland") |>
  addLayersControl(baseGroups = c("Population", "Cropland"),
                    options = layersControlOptions(collapsed = FALSE))

Key Findings

Temperature

1. Accelerating Warming Trend

  • All governorates show clear warming trends since 1960
  • Total warming ranges from approximately 1.5°C to 2.5°C depending on location
  • The warming trend has accelerated since 2000, with the warmest years concentrated in the 2010s and 2020s

2. Regional Variations

  • Cairo and Alexandria (Lower Egypt): Show strong warming with significant heat stress implications for dense urban populations
  • Assiut and Suhag (Upper Egypt): Experience higher absolute temperatures and larger temperature swings

3. Population Weighting Matters

  • Population-weighted temperatures often differ from unweighted averages by 0.2-0.5°C
  • These differences reflect spatial variation in population distribution and climate within governorates
  • Population-weighted measures are more relevant for assessing human exposure to climate change

Precipitation

1. High Inter-Annual Variability

  • Rainfall shows substantial year-to-year variation, with coefficients of variation typically exceeding 30%
  • This variability poses challenges for agriculture and water resource management

2. Spatial Patterns

  • Alexandria: Highest absolute rainfall among study governorates (Mediterranean climate)
  • Cairo: Moderate rainfall with increasing aridity
  • Assiut and Suhag: Lowest rainfall (arid/hyper-arid zones) with extreme sensitivity to precipitation changes

3. Extreme Events

  • Both extreme dry and extreme wet years have occurred across all governorates
  • Recent decades show no clear long-term trend in total annual precipitation, but high variability persists
  • The timing and intensity of extreme events are critical for agricultural systems and water availability

Methodology Notes

Z-Scores and Climate Shocks

Z-scores normalize deviations by historical variability:

\[Z = \frac{X_{year} - \mu}{\sigma}\]

Where: - \(X_{year}\) is the annual value for a given year - \(\mu\) is the mean for the baseline period - \(\sigma\) is the standard deviation for the baseline period

Z-scores beyond ±2 indicate values more than 2 standard deviations from the historical norm, representing exceptional events.

Population Weighting

Population-weighted climate variables are calculated by weighting each grid cell’s value by its population:

\[X_{weighted} = \frac{\sum_i X_i \times Pop_i}{\sum_i Pop_i}\]

This approach better represents human exposure to climate changes than simple spatial averages.

Data Availability

All climate data and analysis code are available in the project repository:

  • Temperature Data: Data/intermediate/Governorate Data/era5_temp_19602024.Rds
  • Precipitation Data: Data/intermediate/Governorate Data/chirps_prec_yearly_19812024.Rds
  • Analysis Code: Code/4_EDA/ClimateChange_Report.Rmd

Report generated on 2026-02-25 using R version 4.5.2